Designing a high-resolution photorealistic face generator

WHAT THIS TESTS: Whether you can compare generative families on a concrete hard problem and reason about quality, controllability, speed, multi-scale detail, and diversity rather than just naming a model.

A GOOD ANSWER COVERS: Frame the trade-off. A style-based GAN such as StyleGAN excels at photorealistic faces: it maps the latent through a mapping network into an intermediate, more disentangled space, then injects styles at every resolution via adaptive normalization, so coarse layers control pose and identity while fine layers control skin texture and hair, giving strong control and one-shot fast sampling. Its risks are training instability and mode collapse that can hurt diversity, plus characteristic artifacts that later versions specifically fixed. A diffusion approach trades sampling speed for very stable training, excellent mode coverage, and state-of-the-art fidelity; to reach 1024 resolution efficiently you would operate in a latent space or use a cascade that generates low resolution then super-resolves. For multi-scale detail, either progressive or coarse-to-fine generation ensures global structure forms before fine texture, avoiding blurry or incoherent results. For diversity, with a GAN use minibatch standard deviation, truncation chosen carefully so you do not over-trim variety, and a varied dataset; with diffusion, diversity comes more naturally but watch the guidance scale, since high guidance sharpens fidelity while shrinking variety.

COMMON WRONG ANSWERS: Picking one architecture with no justification; ignoring multi-scale detail entirely; forgetting diversity and mode collapse; claiming GANs are always faster and better with no caveats; proposing a plain GAN with no progressive or style mechanism for 1024 output; over-truncating and then wondering why faces look the same.

LIKELY FOLLOW-UPS: How does the truncation trick trade fidelity for diversity? How would you evaluate, and why does FID alone miss some failures? How do you mitigate dataset bias in faces? Latent versus pixel diffusion for 1024, and the compute budget?

ONE CONCRETE EXAMPLE: For an avatar product needing instant interactive sampling and slider-style control over age and expression, choose a style-based GAN: coarse style layers set identity and pose, fine layers add pores and stray hairs, and a modest truncation keeps faces realistic while minibatch standard deviation preserves variety. If instead the priority is maximum diversity and training stability over a noisy dataset, a latent diffusion cascade with moderate guidance is the safer choice despite slower sampling.